



"""simple docstring"""






snake_case   					=				[0, 2, 4, 6, 8]
snake_case   					=				[1, 3, 5, 7, 9]




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				int:
  if remaining_length == 0:
    if digits[0] == 0 or digits[-1] == 0:
      return 0

    for i in range(length // 2 - 1  ,						-1  ,						-1	):
      remainder += digits[i] + digits[length - i - 1]

      if remainder % 2 == 0:
        return 0

      remainder //= 10

    return 1

  if remaining_length == 1:
    if remainder % 2 == 0:
      return 0

    _snake_case   	=      0
    for digit in range(10	):
      _snake_case   	=      digit
      result += reversible_numbers(
          0  ,						(remainder + 2 * digit) // 10  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    return result

  _snake_case   	=      0
  for digita in range(10	):
    _snake_case   	=      digita

    if (remainder + digita) % 2 == 0:
      _snake_case   	=      ODD_DIGITS
    else:
      _snake_case   	=      EVEN_DIGITS

    for digita in other_parity_digits:
      _snake_case   	=      digita
      result += reversible_numbers(
          remaining_length - 2  ,						(remainder + digita + digita) // 10  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)
  return result




def 			snake_case		(		lowerCAmelCase_ = 9	)						->				int:
  _snake_case   	=      0
  for length in range(1  ,						max_power + 1	):
    result += reversible_numbers(lowerCAmelCase_  ,						0  ,						[0] * length  ,						lowerCAmelCase_	)
  return result


if __name__ == "__main__":
     print(F"{solution() = }")













"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"